With advances in technology driven by artificial intelligence (AI) and the creation of data lakes, organizations are coming to recognize their value to industrial production.
Enterprise AI can be embedded in fundamental business models to augment decision-making. It focuses on outcomes rather than the technology itself, enabling an organization to turn data into valuable insights for creating continuous customer value.
The metal industry, one of the oldest in human civilization, has been the backbone of modern industrial growth. Steel is the most popular metal in use today, and iron, the fourth most common element in the Earth’s crust, is its key constituent.
According to the Worldsteel Association, global crude steel production increased from 189 million metric tons in 1950 to 1.8 billion mt in 2018. Rapid growth over the past two decades came from excess capacity produced in China, which contributes nearly 50 percent of the world’s steel production. The mismatch has caused major disruptions to industry, especially in the western world, as Chinese manufacturers began exporting their excess inventory at low prices.
While this imbalance is likely to continue, companies are working to improve efficiency through modernizing their iron and steelmaking technologies. In the process, they have gradually reduced dependency on human labor, in favor of automation.
A modern steel plant employs far less human labor than 25 years ago. During a period when the world’s steel production grew by two and a half times, the industry has shed more than 1.5 million members of the workforce.
The steel supply chain contains some unique elements that are core to the industry:
Enterprises are generating large volumes of data daily, and it’s growing exponentially. Data comes in both structured and unstructured forms. As in-memory computing, storage, and digital technologies become reliable and affordable, many metal companies are using them to develop advanced analytics and gain process insights. Up to now, however, most of those efforts have lacked organization-wide vision in the form of integrated supply chain strategies. The steel industry has significant room to benefit from improving its digital prowess.
A digital twin is the virtual replica of physical supply-chain processes, and the backbone for cyber-physical integration. It ensures the seamless transmittal of data between digital worlds and physical entities. To enable enterprise AI, the following attributes of digital twins are necessary:
A big-data lake is the single place of storage for all enterprise data in its native format. It can be used for a variety of purposes, such as data science-driven advanced analytics and machine learning. For steel companies, a big-data lake can store unrelated business data from various supply-chain nodes, including pits, yards, blast furnaces, casters and mills, in raw formats. Big data can be used to obtain insights in the following areas:
Enterprise AI comprises the following functions:
Enterprise AI drives reliability, efficiency and productivity in the steel industry through reduction of manual labor, replacing it with machine-to-machine connectivity and prescriptive analytics. It can sense elements such as market insight, demand volatility, and disruptions in production and supply. The industrial use of AI technologies, along with investments in big-data lakes and digital twins, promises to transform steel companies into more responsive and profitable operations. A pragmatic view of enterprise AI can dramatically increase steel supply-chain efficiencies, leading to reduced inventory carrying costs and shortening time to market in the volatile steel marketplace.
Hiranmay Sarkar is a managing partner with Tata Consultancy Services’ (TCS) Consulting and Services Integration Practice.
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